Look Where It Matters: High-Resolution Crops Retrieval for Efficient VLMs

• URL: http://arxiv.org/abs/2603.16932v1
• Date: Sat, 14 Mar 2026 10:11:32 GMT
• Title: Look Where It Matters: High-Resolution Crops Retrieval for Efficient VLMs
• Abstract summary: Vision-language models (VLMs) typically process images at a native high-resolution, forcing a trade-off between accuracy and computational efficiency.<n>We present AwaRes, a framework that operates on a low-resolution global view and using tool-calling to retrieve only high-resolution segments needed for a given query.
• Score: 28.88727946733177
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Vision-language models (VLMs) typically process images at a native high-resolution, forcing a trade-off between accuracy and computational efficiency: high-resolution inputs capture fine details but incur significant computational costs, while low-resolution inputs advocate for efficiency, they potentially miss critical visual information, like small text. We present AwaRes, a spatial-on-demand framework that resolves this accuracy-efficiency trade-off by operating on a low-resolution global view and using tool-calling to retrieve only high-resolution segments needed for a given query. We construct supervised data automatically: a judge compares low- vs.\ high-resolution answers to label whether cropping is needed, and an oracle grounding model localizes the evidence for the correct answer, which we map to a discrete crop set to form multi-turn tool-use trajectories. We train our framework with cold-start SFT followed by multi-turn GRPO with a composite reward that combines semantic answer correctness with explicit crop-cost penalties. Project page: https://nimrodshabtay.github.io/AwaRes

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